target="_blank" rel="nofollow" href="#ulink_b4d69138-497c-511c-87d2-ae0cbdd41c6e">Figure 1.10 shows the Heat Map for all the parameters for the days 11th, 12th, and 13th December, 2017, at Sector 62, Noida. From the Heat Maps it could be observed that PM2.5 is the main pollution causing parameter in the Air. It could also be observed that it remains at dangerous levels on all days and during Days as well as Nights. Figure 1.11 shows the Heat Map for all the parameters for the days 6th, 7th and 8th June, 2020 at Sector 62, Noida. The reduced levels of all pollutants could clearly be seen from the Heat Map as a result of imposed lockdown. However, PM2.5 still remains the top contributing factor toward pollution in the area.
Figure 1.12 shows the predicted values of O3 for Anand Vihar, New Delhi in December, 2017, and decline in O3 levels can be observed. Figure 1.13 shows the predicted values of PM10 for Sector 62, Noida in June, 2020, and decline in levels could be observed.
The quality of air as shown in Figure 1.14 could also be observed/predicted for the major cities of India. This helped the user to study the quality of air throughout the country. The figure shows the quality of air as severe (magenta), very poor (yellow), poor (cyan), moderate (red), satisfactory (green), and good as blue dot on the map. It was realized that smaller cities, towns, and villages in India have good air quality. It is only the Metropolitan cities and the areas surrounding these cities that suffer from worst air quality.
From our project, we had some major findings. It was found that the values of different parameters of air depend on the latest past records (few days to a month) and not on many previous months. While retrieving real-time values through API for different parameters, sometimes, null or zero values occur. This might be due to malfunctioning of the sensors or inappropriate weather conditions. Zero or very less values might also occur at night because of the fact that certain parameters like O3 mix with other chemical compounds to form other compounds and consequently their value reduces. No2 and SO2 are also sometimes interacting and hence their abrupt values. The raw data is much easier to understand through visualizations for a common man. Also, lockdown is expected to be the effective alternative measure to be implemented for controlling air pollution.
Figure 1.3 Screenshot of fetched data.
Table 1.2 Precision, recall, and F1-score.
| Classes | Precision | Recall | F1-Score |
| Moderate | 1.0 | 0.99 | 0.99 |
| Poor | 1.0 | 0.95 | 0.97 |
| Satisfactory | 0.98 | 1.0 | 0.99 |
| Severe | 1.0 | 1.0 | 1.0 |
| Very Poor | 1.0 | 1.0 | 1.0 |
| Avg/total | 0.99 | 0.99 | 0.99 |
| Final Accuracy: 0.9893 | |||
Table 1.3 MAE and RMSE scores for different epochs.
| Test MAE for 1 | 8.864 |
| Test RMSE for 1 | 12.122 |
| Test MAE for 2 | 17.996 |
| Test RMSE for 2 | 35.390 |
| Test MAE for 3 | 23.820 |
| Test RMSE for 3 | 35.938 |
| Test MAE for 4 | 6.021 |
| Test RMSE for 4 | 9.269 |
Figure 1.4 Predicted values in Bengaluru in December, 2017.
Figure 1.5 Predicted values in Bengaluru in June, 2020.
Figure 1.6 Predicted values in New Delhi in December, 2017.
Figure 1.7 Predicted values in New Delhi in June, 2020.
Table 1.4 MAE scores for LSTM hyper parameters.
| Batch size | Epochs | NO2 | O3 | PM10 | PM2.5 | SO2 |
| 10 | 10 | 22 | 52 | 142 | 64 | 14 |
| 24 | 100 |
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